* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download - Cal State LA - Instructional Web Server
Survey
Document related concepts
Pharmaceutical industry wikipedia , lookup
Discovery and development of beta-blockers wikipedia , lookup
5-HT3 antagonist wikipedia , lookup
Drug interaction wikipedia , lookup
Psychopharmacology wikipedia , lookup
Discovery and development of antiandrogens wikipedia , lookup
Nicotinic agonist wikipedia , lookup
Cannabinoid receptor antagonist wikipedia , lookup
Drug discovery wikipedia , lookup
Discovery and development of angiotensin receptor blockers wikipedia , lookup
Neuropharmacology wikipedia , lookup
Drug design wikipedia , lookup
Transcript
Structure Prediction of human Histamine 4 Receptor (hH4R) Charlie Seto, Bioinformatics Summer Institute, CSULA Ravi Abrol & Soo-Kyung Kim, MSC, CalTech William Goddard, MSC, CalTech Outline Why Study GPCRs? Objective H4R Structure Prediction & Building Ligand docking Future Work “Core of Modern Medicine” GPCRs have cell signaling functions 40% of new drugs target GPCRs Novartis: Zelnorm (5-HT4) Eli Lilly: Zyprexa (5-HT2) Schering-Plough: Clarinex (H1) GlaxoSmithKline: Zantac (H2) (Source: Modern Drug Discovery, “It’s a GPCR World”, Nov 2004) H4R (Histamine H4 Receptor) All H4 functions still not known Together with H1, asthma? H4 is unique Dissimilar to H1, H2, H3 Known Histamine receptor ligands weak against H4R. New ligands needed to optimize drugs! Or, to increase specificity Histamine Objective of your Project Predict 3D structure of Histamine H4 Receptor (H4R) from AA sequence Validate structure with known ligands, Histamine etc. Good structure for drug design Other questions Difference of H4R vs. other Histamine receptors? TM Prediction Perform BLAST with H4R sequence “Broad search”, E-value = 0.1 Run multiple-sequence alignment Develop hydrophobicity profile by averaging BLASTed sub-profiles Prediction of TM regions based on hydrophobicity Get hydrophobic center, controls TM “depth” Hydrophobicity Profile Hydrophobicity Profile of H4R from predictm, window=1 0.8 0.6 Hydrophobicity Value 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 -1.2 TM2 poorly defined Large hydrophilic domain implies cytosolic loop hydrophobicity, residue hpc, raw geometric center Other PredictM output PredicTM’s BLAST output Nearest relative was H4R in Mouse & Rat ~79-80% Histamine 3-R, paralog ~55% identity, #6 Human β-2 Adrenergic (template) TM identity (#1 & #2) ~33% identity, #179 Bovine Rhodopsin (template) ~22% identity, #1249 “Final” TM Sequence TM TM TM TM TM TM TM 1: 2: 3: 4: 5: 6: 7: 15-RVTLAFFMSLVAFAIMLGNALVILA-39 51-SYFFLNLAISDFFVGVISIPLYIPHTLFEWDFGK-85 88-VFWLTTDYLLCTASVYNIVLISY-111 131-VLKIVTLMVAVWVLAFLVNGPMILV-155 169-EWYILAITSFLEFVIPVILVAYFNMNIYWS-203 303-KSLAILLGVFAVCWAPYSLF-327 336-KSVWYRIAFWLQWFNSFVN-359 Helix Kinking Have: TM sequences Need: 3D structure First: Predict helix kinking Pro71 Helix kinking caused by Pro & Gly residues, stabilized by Ser & Thr Right: TM2, minrmsd method. Pro75 Helix Building Compare helices to template (human β2 ADR) Template determines angle of insertion into membrane, orientation of TMs 0° 15° 30° Rotation Bihelix Pairing: Adjacent helices paired off, rotated (12 combinations) Bihelix data aggregated to build 7TM structures Visual Analysis Visually check H-bonds Check for GPCR motifs NPxxY 1-2-7 networks, etc Structure Activity Data Example: Mutate Asp94…no activity, therefore…Asp94 important! (Assess angle and coordinate) Asn147 (4.57) Theorized imidazole binding pocket Glu182 (5.46) Asp94 (3.32) Ligands “Build” in Maestro Special pre-reqs for selecting ligands Need experimental data for binding Big Picture: Ligands are to validate structure! Imetit, agonist Current Progress Analysis continues Missing expected interhelical interactions Causes being investigated A highly polar motif on TM2 may have shifted the structure Not found on other Histamines, or templates Will continue with pre-docking Assess ligand affinity data after docking Determine if structure “good/bad” by comparison to experimental data Special Thanks Mentor: Ravinder Abrol & Soo-Kyung Kim, MSC, Caltech PI: William Goddard, MSC, Caltech Other members of Biogroup, MSC The SoCalBSI faculty team Dr. Sandra Sharp Dr. Wendie Johnston Dr. Jamil Momand Dr. Nancy Warter-Perez …and others Funding provided by: References D. FILMORE. “It’s a GPCR World” Modern Drug Discovery Nov 2004 N. SHIN, E. COATES, N. J. MURGOLO, K. L. MORSE, M. BAYNE, C. D. STRADER, and F. J. MONSMA, JR. “Molecular Modeling and Site-Specific Mutagenesis of the Histamine-Binding Site of the Histamine H4 Receptor” Mol Pharmacol 62:38–47, 2002